Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vignesh2027/AI-AGENT-SKILLS --skill incident-responsegit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vignesh2027/ai-agent-skills/incident-response)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/incident-response"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/incident-response/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/incident-response"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/incident-response.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00993 |
| Opus 5.5 | $0.00007 | $0.00397 |
| Sonnet 5.5 | $0.00004 | $0.00199 |
| Haiku 4.5 | $0.00002 | $0.00099 |
Grade A, and why
incident-response scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Incidents are inevitable. How you respond determines whether they are learning opportunities or recurring nightmares. This skill gives you a structured process from first alert to prevention of recurrence.
When to Use
- When an alert fires indicating user impact
- When a deployment causes degraded service
- When you discover a data issue in production
- After any unplanned outage, even brief
Process
Phase 1: DETECT (0–5 minutes)
Step 1: Acknowledge the alert Acknowledge in your alerting system within 5 minutes. This signals you're on it and stops duplicate escalations.
Step 2: Assess severity
- P0 — Complete service outage, data loss, security breach. Page everyone.
- P1 — Significant degradation affecting many users. Page the team.
- P2 — Partial degradation, workaround exists. Handle during business hours.
- P3 — Minor issue, users unaffected or workaround obvious. Normal ticket.
Step 3: Declare the incident and open a channel For P0/P1: open an incident channel (#incident-YYYY-MM-DD-brief-description). All communication goes there. Appoint an Incident Commander (IC).
Phase 2: COMMUNICATE (5–15 minutes)
Step 4: Post a status update Within 15 minutes of detection, post to the status page: "We are investigating reports of [symptom]. We will provide an update in 30 minutes." Do not wait until you have a fix. Users need to know you know.
Step 5: Brief stakeholders Notify: on-call manager, customer-facing teams, affected customers (for P0/P1). Use the incident channel as the single source of truth.
Phase 3: MITIGATE (15 minutes – resolution)
Step 6: Stop the bleeding first Mitigation before diagnosis. Options:
- Roll back the last deployment
- Disable the feature flag
- Scale up capacity
- Redirect traffic away from the failing component
Don't spend time diagnosing the root cause while users are impacted. Mitigate first.
Step 7: Diagnose Once impact is reduced, find the root cause:
- What changed recently? (Deployments, config changes, traffic spikes)
- What do the metrics show? (Error rate, latency, saturation)
- What do the logs show? (First error, error pattern, affected user IDs)
- What do the traces show? (Where in the call chain is the failure?)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 105 lines · 0 tokens per session scan A 13ae65799a8e
incident-response is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 993 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
incident-response compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| incident-responsevignesh2027/AI-AGENT-SKILLS | Skill | 2 | 2mo ago | MIT | A | 18 | Claude Code, Codex |
| stuckQwenLM/qwen-code | Skill | 29k | today | Apache-2.0 | A | 57 | Claude Code |
| structured-debuggingQwenLM/qwen-code | Skill | 29k | today | Apache-2.0 | A | 85 | Claude Code, Codex |
| test-failure-explorerQwenLM/qwen-code | Skill | 29k | today | Apache-2.0 | A | 32 | Claude Code, Codex |
| rust-checkHmbown/CodeWhale | Skill | 41k | 6d ago | MIT | A | 15 | Claude Code, Codex |
| debugHmbown/CodeWhale | Skill | 41k | 6d ago | MIT | A | 30 | Claude Code, Codex |
| feedbackHmbown/Codewhale | Skill | 41k | 1mo ago | MIT | A | 0 | Claude Code, Codex |